Information extraction (IE) refers to the problem of extracting structured information from unstructured or semi-structured text. It has been well-studied in the realm of Natural Language Processing. In recent years, IE has emerged as a critical building block in a wide range of enterprise applications, including financial risk analysis, social media analytics and regulatory compliance, among many others. An important practical challenge driven by the use of IE in these applications is usability, particularly, how to enable the ease of development and maintenance of high-quality information extraction rules, also known as annotators, or extractors.
Generally, the development of extractors presents itself as a notoriously labor-intensive and time-consuming process. In order to ensure highly accurate and reliable results, this task is traditionally performed by trained linguists with domain expertise. As a result, extractor development is regarded as a major bottleneck in satisfying the increasing text analytics demands of enterprise applications.